Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal E-Jurnal Matematika The Journal of Experimental Life Sciences (JELS) Telematika : Jurnal Informatika dan Teknologi Informasi Journal of Innovation and Applied Technology Natural B Jurnal Berkala Epidemiologi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknoka Jurnal Irigasi Indonesian Journal of Electrical Engineering and Computer Science COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Anam, Syaiful
Brawijaya University
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
The Artificial Bee Colony (ABC) Algorithm for Estimating Parameter of Epidemic Influenza Model Ririn Nirmalasari; Agus Suryanto; Syaiful Anam
The Journal of Experimental Life Science Vol. 10 No. 1 (2020)
Publisher : Postgraduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1048.636 KB) | DOI: 10.21776/ub.jels.2019.010.01.06

Abstract

The Artificial Bee Colony (ABC) is one of the stochastic algorithms that can be applied to solve many real-world optimization problems. In this paper, The ABC algorithm was used to estimate the parameter of the epidemic influenza model. This model consists of a differential system represented by variations of Susceptible (S), Exposed (E), Recovered (R), and Infected (I). The ABC processes explore the minimum value of the mean square error function in the current iteration to estimate the unknown parameters of the model. Estimating parameters were made using participation data containing influenza disease in Australia, 2017. The best parameter chosen from the ABC process matched the dynamical behavior of the influenza epidemic field data used. Graphical analysis was used to validate the model. The result shows that the ABC algorithm is efficient for estimating the parameter of the epidemic influenza model. Keywords: ABC, Epidemic, Estimate, Influenza, Parameter.
Penerapan Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Notasi Simplified Molecular Input Line System (SMILES) Suhhy Ramzini; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1367.811 KB)

Abstract

Active compound is a substance (medicine) capable of providing kind effect when the human bodies are in bad shape. Active compound often used for preventing or curing a disease. Active compound takes an important role in medical world. Simplified Molecular Input Line System notation, in short SMILES notation is representation of compound (carbon bond) created by David Weininger in 1980. SMILES notation composed of ASCII (American Standard Code for Information Interchange) characters so that it can be stored in string variable and easily processed by the computer. Currently, there are numbers of compounds (SMILES notation) and it makes the classification for tested compound that can be made into a medicine (active compound) becomes necessary. The purpose of this research is to classify the active compound function utilizing SMILES notation with Learning Vector Quantization (LVQ) method by using 2 active compound function classes, one for metabolic disease, and another for cancer disease. There are 467 datasets with each 11 features. On testing process, the obtained value for learning rate is 0.1, decrement alpha is 0.3, minimum alpha is , and maximum epoch is 15 by using a percentage of 80% training data and 20% testing data which produce accuracy of 76.34%.
Implementasi Fuzzy K-Nearest Neighbor (FK-NN) Untuk Mengklasifikasi Fungsi Senyawa Berdasarkan Simplified Molecular Input Line Entry System (SMILES) Raden Rizky Widdie Tigusti; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.598 KB)

Abstract

The active compound is a chemical compound that has many functions. One of the functions of the active compound is as a medicine. Active compounds have special characteristics that determine function as a drug. To obtain a characteristic value on the active compound SMILES notation are used as input system. SMILES notation is a modern chemical notation that can be stored on string variables to use for the process of computing. To obtain the characteristic on the compound the SMILES notation will be divided into 12 features consisting of B, C, N, O, P, S, F, Cl, Br, I, OH and the length from SMILES notation. The value of each feature is obtained from the preprocessing process against the SMILES notation made at the beginning of the classification process.In the process of classifying the function of active compounds, the Fuzzy K-Nearest Neighbor method are used because it can do process by using large amounts of data. The Fuzzy K-Nearest Neighbor method is a combination of two methods namely Fuzzy and K-Nearest Neighbor. An important step of the classification process using the Fuzzy K-Nearest Neighbor is to calculate the distance from each test data to the train data or so-called by euclidean distance, pick value as much as k value and calculate the fuzzy. Tests in this study using the dataset as much as 631 and divided into 2 as the data train and test data. Each composition of data training and data testing are 80% (503 data) and 20% (128 data). The result of the accuracy is 71% with the value of k = 15, in other test by using k-fold cross validation the biggest accuracy is 77%.
Pengelompokan Fungsi Aktif Senyawa Data SMILES (Simplified Molecular Input Line Entry System) Menggunakan Metode K-Means Dengan Inisialisasi Pusat Klaster Menggunakan Metode Heuristic O(N LogN) Sherly Witanto; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.274 KB)

Abstract

Active compounds have function as a medicine that can prevent or cure diseases. Some of the active compounds have been known the function and some are still in the research stage. Currently in Indonesia there is still no program that capable to classifying chemical compounds as drugs for certain diseases. SMILES notation is the conversion of chemical compounds in the form of line notation. Notation SMILES able to provide convenience to the process of computerization on the classification of chemical compounds. The classification of the SMILES notation is carried out by taking the values ​​of the B, S, N, O, I, F, C, P, Cl, Br and OH atoms present in the compound. Before being processed, to get the value of the feature is done by dividing the process of each atom with the length of the compound. K-Means algorithm is the most widely used clustering method because it is easy and simple. The grouping of active function using K-Means method has weakness in random cluster initialization process, so that heuristic method o (n logn) is used to get the cluster initials with better value. Based on the software that has been made, the test is done using 512 of training data and test data as much as 128. Accuracy obtained from the test that is equal to 63% and testing using ¬K-Fold Cross Validation with 10 times the test produces an average accuracy of 52,58 %. Testing using K-Means with heuristic o (n logn) yielded better accuracy compared to conventional K-Means.
Penerapan Algoritme C4.5 untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Kode Simplified Molecular Input Line System (SMILES) Mochammad Iskandar Ardiyansyah Rochman; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.802 KB)

Abstract

Compounds are things that are often found in this world, with a substance that is a collection of compounds (Educated, 2015). The compound itself is divided into active and inactive compounds. The compound has a function that may be utilized for some aspect if it has a function like a drug or a stimulating hormone work. notation of SMILES (Simplified Molecular Input Line System) by David Weininger in 1980. SMILES notation takes advantage of ASCII characters that are very easy to process by the computer. SMILES notation classification process will be very useful to know the function class of the compound. This study was conducted to classify the function of the compound utilizing the SMILES notation by applying the C4.5 algorithm while the object is 2 classes of compound function, including the class of cancer and metabolism. Features tested from research as many as 11 features. The results of the best tests when the discretization technique is performed using entropy based discretization techniques, dividing the SMILES notation values ​​on each feature attribute, and the use of practicable data as much as possible will result in an accuracy of 79.34%. While the accuracy of the cross validation test shows an accuracy of 70.18%.
Implementasi Gabungan Metode K-Means Learning Vector Quantization (LVQ) Untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Data SMILES Nur Khilmiyatul Ilmiyah; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (964.381 KB)

Abstract

The active compound is a chemical compound that has many functions. While the inactive compound, doesn't have much function only as additional substances. Active compounds can be divided into two therapeutic functions as alternative medicine, and Pharmacology function to control drug containing the active compounds in it. In order to get functions in the active compounds used notation SMILES. SMILES notation is a representation of the active compounds with modern chemical notation, so that the computer can read the elements of the compound. Of the many SMILES notations at this time, all the SMILES notations cannot be used as medicine because they are still in the testing phase. SMILES notation that has been tested could be used as medicine. Therefore, this research will be built a fixed classification model that takes into account all the data. Based on the test results, the K-Means method of combined Learning Vector Quantization (LVQ) generate value accuracy of 72.22%, K-means conventional 52.65%, while Learning Vector Quantization (LVQ) owns 67.96%. The results show that the combined K-Means method of Learning Vector Quantization (LVQ) have better results than conventional K-means and Learning Vector Quantization (LVQ).
Momentum Backpropagation Untuk Klasifikasi Fungsi Senyawa Aktif Berdasarkan Notasi SMILES (Simplified Molecular Input Line Entry System) Nyimas Ayu Widi Indriana; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (764.799 KB)

Abstract

Active compounds can be used to make certain drugs and very important in the medical sector. Classification of active compounds is the most important thing in making medicines. After classifying the active compound, it is continued with the process of making and testing drugs that require a variety of tools. The cost of making and testing these drugs requires a high cost and time. This is a major obstacle for medical experts to make certain medicines. By utilizing current technology, a system can be made to classification process of active compounds, so the performance of medical experts for making certain drugs can be faster. The classification process can be done by using a computer and utilizing the SMILES notation. SMILES notation allows a compound to be processed by a computer. The momentum Backpropagation method can be used to perform the classification process properly. Based on the program that has been made, there are 4 types of testing using 522 training data and 131 test data producing, the best accuracy of 70,99% with a learning rate of 0,00001, max epoch of 100, momentum of 0,25 and hidden layer neurons of 4.
Klasifikasi Fungsi Senyawa Aktif Data Berdasarkan Kode Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Modified K-Nearest Neighbor Yunita Dwi Alfiyanti; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.298 KB)

Abstract

Compounds are single chemical substances from two or more chemical elements that form bonds and can be described. The compound is divided into active compounds and inactive compounds. Active compounds are chemical compounds that have pharmacology or usability. Compounds have an arrangement that is difficult to process on a computer, for which code is created that is easy to process using a computer. The code is a SMILES (Simplified Molecular Input Line Entry System) which is a code of modern chemical bonds that will be converted into a line to facilitate the classification process in the system. The special character of SMILES is obtained by doing preprocessing with the results of 11 features consisting of B, Br, C, Cl, F, I, N, O, P, S and OH atoms. These features are then used for the classification process using the Modified K-Nearest Neighbor method, where this algorithm is the development of the KNN method which consists of two processing, training data validation and weighting. The classification of the function of active compounds aims to facilitate the grouping of active compounds based on their pharmacology through the help of information technology and computer science degeneration, which so far in the medical field requires a long time in its determination because it uses laboratory tests. Tests that have been conducted using 260 data are divided into 2 categories of classes, namely the Neural class and the Heart class which consists of 90% (234 data) training data and 10% (26 data) test data. The test gets results in the form of an accuracy value of 73% with a k value of 3, whereas in the k-fold cross validation test the value of accuracy is obtained an average of 62.69%.
Klasifikasi Senyawa Kimia dengan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Extreme Learning Machine (ELM) Isti Marlisa Fitriani; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.401 KB)

Abstract

Indonesia has a huge natural's potential by the existence of various plants and animals discovery. This issue brings a good for Indonesian people through taking advantage of nature, especially in pharmacology. In pharmacology, active compounds can be used to prevent and cure disease. Therefore, a research is conducted in informatics's field by making an active compounds' classification system to determine its pharmacological benefits. SMILES is a chemical compound notation used in this research. SMILES's features which are used as many as 15, namely B, C, N, O, P, S, F, Cl, Br, I, OH, @, =, #, and (). ELM is an ANN method that can do a generalization better than conventional methods in a limited time. A number of hidden neurons test which were conducted using k-fold cross validation method in 2 classes produced the best accuracy, 85,03%, in Metabolism and Inflammation class scenario with a total of 5, 10, and 15 hidden neurons. A number of hidden neurons' test use k-fold cross validation method which were conducted in 3 classes produced the best accuracy, 55,06%, in Metabolism, Inflammation, and Cancer class scenario with a total of 300 hidden neurons. The best accuracy was obtained as many as 55,06% by testing 15 features with 300 hidden neurons, while in 11 features's test with 400 hidden neurons was found a number of 49,18% as the best accuracy.
Klasifikasi Fungsi Senyawa Aktif Berdasarkan Notasi Simplified Molecular Input Line Entry System (SMILES) Dengan Metode K-Means Naive Bayes (KMNB) Revi Anistia Masykuroh; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1067.697 KB)

Abstract

Indonesia is a tropical country that has the most biodiversity in the world. Almost all of the plants part like leaf, root, stem, fruit, flowers, seeds, and rhizome can be used for human health. In Indonesia the utilization of plants as medicine is so limited. Therefore, further research and continuous plant drugs or herbal remedies is really needed as well as the technologies are able to maximize the utilization. In 1980, David Weininger found a chemical notation for processing informations that related to a modern chemistry named Simplified Molecular Input Line System (SMILES) and that notation is specifically for computer used. On this research, K-Means Naive Bayes methods are used for the classification of the functions of the active compounds because this methods are able to grouping data according to their similarity and the classification process is much easier to understand. Based on the test results, the K-Means Naive Bayes are abled to give an accuracy system 85.45% with a 80% training data ratio and 20% testing data. The system also being tested using K-Fold Cross Validation with K-Fold as many as 10, the highest accuracy that can be given is 86.66% on 9th fold and the lowest is 70.37% on 1st fold. While the average of accuracy using the K-Fold Cross Validation is 82.6%.
Co-Authors A.S. Adhiwibawa, Marcelinus Abu Bakar Sambah Agus Suryanto Agus Suryanto Agus Widodo Ahmad Afif Supianto Candra Dewi Corina Karim, Ari Andari Dian Eka Ratnawati Dwi Agus Kuncoro Dwi Febry Indarwati Dwi Mifta Mahanani Faiz Anggiananta Winantoro Fery Widhiatmoko Helen Yuliana Angmalisang Hidayat, Noor Indah Yanti Isti Marlisa Fitriani Ivan C. Onggara Kestrilia Rega Prilianti Marcelinus A.S. Adhiwibawa Mochamad Hakim Akbar Assidiq Maulana Mochamad Hakim Akbar Assidiq Maulana Mochammad Iskandar Ardiyansyah Rochman Mohamad Handri Tuloli Musafir, Raqqasyi Rahmatullah Nur Khilmiyatul Ilmiyah Nyimas Ayu Widi Indriana Onggara, Ivan C. Onky Prasetyo Raden Rizky Widdie Tigusti Revi Anistia Masykuroh Ririn Nirmalasari Sherly Witanto Sobri Abusini Suhhy Ramzini Suryanto, Agus Tatas H.P. Brotosudarmo Trisilowati, Trisilowati Wuryansari Muharini K. Yunita Dwi Alfiyanti Zuraidah Fitriah ZURAIDAH FITRIAH